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Multilevel Thresholding for Image Segmentation based on Similarity Filtering

Affiliations

  • School of Engineering and Technology, The Northcap University, Gurugram − 122017, Haryana, India

Abstract


Objective: In this paper, an effective and fast multi-threshold image segmentation method is proposed based on similarity filtering. Method: The image histogram peaks and the valley can be used to locate the clusters in the image. The idea of the proposed research is to fit the Gaussian distribution to the histogram of the image. Dominant peaks are selected from the input image histogram near to its Gaussian distribution. Then for each element of the peaks, peak’s valleys are obtained in the left (low) and right (high) side. Findings: Experiments on a variety of images from Berkeley Segmentation Dataset (BSD) show that the new algorithm effectively segments the image in a computationally efficient manner. Comparison/ Performance evaluation: On comparison, proposed approach is found to be better than other existing methods. Peak Signal to Noise Ratio (PSNR) and time are used to evaluate the performance. The proposed algorithm tries to fit Gaussian curves on the dominant peaks and thus find the valleys which are used as thresholds. Novelty: This is always a quicker process as there is a predefined model which only needs to be fit for the given data set.

Keywords

Gaussian Distribution, Image Segmentation, Multilevel Thresholding, Similarity Filtering.

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References


  • Arora S, Hanmandlu M, Gupta G, Singh L. Enhancement of Overexposed Color Images. Proceedings of ICoICT, Malaysia; 2015, p. 207-11.
  • Gupta G, Kumar M. An Iterative Marching with Correctness Criterion Algorithm for Shape from Shading under Oblique Light Source. Proceedings SocProS, India; 2012. p.28-30.
  • Hanmandlu M, Arora S, Gupta G, Singh L. A Novel Optimal Fuzzy Color Image Enhancement using Particle Swarm Optimization. Proceedings of IC3, India; 2013. p. 41-46.
  • Lew M. Content-based Multimedia Information Retrieval: State of the Art and Challenges, ACM Transactions on Multimedia Computing, Communications, and Applications.2006 Feb; 2(1):1–19.
  • Graves, G. Batchelor MB. Machine Vision for the Inspection of Natural Products. Springer, 2003.
  • Wu W, Chen AYC, Zhao L, Corso JJ. Brain Tumor Detection and Segmentation in a CRF Framework with Pixel-Pairwise Affinity and Super Pixel-Level Features, International Journal of Computer Aided Radiology and Surgery. 2014 March; 9(2):241-53.
  • Delmerico JA, David P, Corso JJ. Building Façade Detection, Segmentation and Parameter Estimation for Mobile Robot Localization and Guidance. Proceedings of IROS: USA, 2011, p. 1632-39.
  • SRI International Sarnoff Launches Iris Biometric Vehicle Access Control System. Date accessed. 20/10/2015. Available at: http://www.biometricupdate.com/201401/sri-internationallaunches-new-iris-recognition-access-controlsystem.
  • Lorensen WE, Cline HE. Marching Cubes: A high Resolution 3D Surface Construction Algorithm. Computer Graphics. 1987 July; 21(4):163-69.
  • Sezgin M, Sankur B. Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation, J.Electron. Imaging. 2004 Jan; 13(1):146–65.
  • Otsu N. A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Sys. Man. Cyber. 1979 Jan; 9(1):62–66.
  • Sahoo PK, Soltani S, Wong AKC. Survey: A Survey of Thresholding Techniques, Computer Vision Graphics Image Process. 1988 July; 41:233–60.
  • Tsai DM, Chen YH. A Fast Histogram-Clustering Approach for Multilevel Thresholding, Pattern Recognition Letters. 1992 April; 13(4):245-52.
  • Chen WT, Wen CH, Yang CW. A Fast Two-Dimensional Entropic Thresholding Algorithm, Pattern Recognition, 1994 July; 27(7):885-93.
  • Kirby RL, Rosenfeld A. A Note on the use of (Gray, Local Average Gray Level) Space as an Aid in Thresholding Selection, IEEE Transactions on System Man Cyber-Algorithm for Multilevel Thresholding. 1979; SMC-9(12):860-64.
  • Huang Q, Gai W, Cai W. Thresholding Technique with Adaptive Window Selection for Uneven Lighting Image, Pattern Recognition Letter. 2005 May; 26(6):801–08.
  • Tseng DA, Huang MY. Automatic Thresholding based on Human Visual Perception, Image Vision Computing. 1993 Nov; 11(9):539–58.
  • Silva DVS, Fernando WAC, Kodikaraarachchi H, Worrall ST, Kondoz AM. Adaptive Sharpening of Depth Maps for 3D-TV, Electronics Letters. 2010 Nov; 46(3):546-48.
  • Tomasi, Carlo, Manduchi R. Bilateral Filtering for Gray and Color Images. Proceedings of ICV. India, 1998.
  • Arora, Siddharth. Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm, Pattern Recognition Letters. 2008 Jul; 29(2):119-25.

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